Organizations will be able to develop and deploy Apache Kafka applications without the need to lock in their data with Glassbeam.
Machine data analytics providerGlassbeam, Inc. announced it is upgrading its IIoT analytics platform with the addition of Apache Kafka integration to provide an open source stream processing model. The upgrade will allow organizations to develop and deploy custom machine data analytics apps without having to lock their data in with Glassbeam.
The company says it hopes the enhanced platform will help enterprises to decide whether to build their own platform or invest in a third party one. The platform’s Semiotic Parsing Language (SPL) provides a data transformation and preparation framework for complex machine data. With the addition of Apache Kafka, the platform will now allow customers to connect to any data store in addition to deploying the platform on-site.
“Developers anywhere can now collect and enrich machine data from any source in their organization. With open access to our core platform, developers can use their existing enterprise apps, connectors, and tools to deploy Kafka-based parsed data quickly,” says Puneet Pandit, co-founder and CEO of Glassbeam. “With our open platform, organizations now have the complete freedom to build custom connected-machine applications with Glassbeam without the need to build their own data ingestion platform that may not fulfill their business objectives.”
According to the company’s announcement, key features include access to open standards, improved developer productivity, elimination of unnecessary data preparation burden, better focus on organizational core, and re-use of existing infrastructure investment.
The Glassbeam platform with the new integration with Apache Kafka is now available with Glassbeam 5.7 as an on-site managed service. The pricing varies and is based on retention periods and data processed per day.
SANTA CLARA, Calif., Feb. 15, 2018 /PRNewswire/ -- Glassbeam, Inc., the premier machine data analytics company, announced today that it has successfully built Artificial Intelligence (AI) applications powered by Machine Learning (ML) models for predicting part failures in expensive imaging modalities, allowing healthcare providers to deliver better and more efficient patient care. Business impact of such new applications delivered real time through cloud-based dashboards and rules-based alerts will revolutionize the landscape on how equipment maintenance is performed today by in-house support staff at healthcare providers, independent service organizations (ISOs), and by the OEMs themselves.
"The management of medical machines such as MRI and CT Scanners have taken on a new level of complexity in recent years, due in part to the increased sophistication of equipment and ever-increasing requirements for compliance, safety, reliability and accuracy," said Corey Holtman, President at Gateway Diagnostic Imaging. "Predicting machine health and utilization patterns with help from latest techniques of Artificial Intelligence and Machine Learning is the next frontier to improve operations in Clinical Engineering function. I am pleased to see Glassbeam innovating on this exciting front for healthcare providers."
The first phase of these applications will focus on CAT (Computed Tomography) Scanners that can cost anywhere between $1 million to $2.5 million or more, depending upon the desired image quality in procedures such as CT Angiography (CTA). One of the most expensive parts of a typical CAT Scanner is the X-ray tube provided by OEMs costing anywhere between $150,000 to $200,000. Unfortunately, replacing a CT scanner's X-ray tube today is more an art than a science and is based on a number of ad hoc data inputs based on age of the machine, number of scans performed, image quality rendered amongst other subjective factors. Without proper diagnostics on machine data signals, many companies end up replacing tubes under the gun to ensure machine uptime at all costs. Glassbeam now has the solution allowing a facility to get a warning signal about a week in advance of a potential tube failure. This can alert the clinical engineering staff to become proactive in avoiding unplanned downtime, saving costs, and averting patient re-scheduling at the last minute.
"The number of signals coming from connected machines in the IoT market have surpassed the ability for humans to keep track of them years ago," said Lise Getoor, Professor of Computer Science and Center Director of D3 (Data, Discovery and Decisions) initiative at University of California, Santa Cruz. "I am excited to see Glassbeam, as a supporting member of D3 Center, taking a leadership role in leveraging artificial intelligence to change the rules of the game for the healthcare market."
"The parts replacement industry for global installed base of medical imaging equipment in 2020 is slated to be a $3.6 billion market," said Puneet Pandit, Co-founder and CEO at Glassbeam. "With AI and ML applications based on analyzing millions of sensor readings captured in Glassbeam cloud each day, even with a modest 10% savings, we are ready to make a significant dent on the underlying inefficiencies of support operations, supply chain, parts and material logistics planning for large enterprises in the healthcare market."
Pricing & Availability
Glassbeam plans to roll out new AI powered dashboards bundled into the current pricing model of its Clinical Engineering Analytics (CLEAN™) IIoT blueprint. For more details, contact email@example.com.
Glassbeam Healthcare IIoT Blueprint: Clinical Engineering Analytics – CLEAN™ Blueprint
Visit us for the latest solution demo at Booth # 313 at healthcare industry show ICE 2018 in Las Vegas, February 16-18, 2018.
Glassbeam is the premier machine data analytics company bringing structure and meaning to complex data generated from any connected machine in the Industrial IoT industry. Funded by several ultra-high net worth investors, Glassbeam's next generation cloud-based platform is designed to transform and analyze multi-structured data, delivering powerful solutions on customer support and product intelligence for companies such as IBM, Dell EMC, Novant Health, and Dimension Data. For more information visit http://www.glassbeam.com or follow us on Twitter@Glassbeam.
Head of Marketing
A new AI system could help hospitals keep their expensive medical equipment healthy by scanning for problems before they become expensive to fix.
Data analytics company Glassbeam has announced a series of new AI applications that will help healthcare providers to identify parts failures in hospital MRI and CT scanners.
Using the new systems, doctors and other medical professionals can tap into machine learning algorithms to ensure that expensive, life-saving systems are kept in constant working order. The applications also offer cloud-based dashboards and alerts to transform the equipment maintenance process.
Glassbeam said it wants to help healthcare organisations “deliver better and more efficient patient care”.
It can be an expensive and lengthy process for medical technicians to take systems offline and repair them internally. Meanwhile, predicting or planning for failures can be a challenge in environments where investment is tight, time is critical, and lives are at stake.
Corey Holtman, president at Gateway Diagnostic Imaging, said medical imaging systems are becoming increasingly complex. As a result, AI and IoT systems could revolutionise the healthcare sector, he said.
“The management of medical machines such as MRI and CT scanners has taken on a new level of complexity in recent years, due in part to the increased sophistication of equipment and the ever-increasing requirements for compliance, safety, reliability, and accuracy,” he said.
“Predicting machine health and utilisation patterns – with help from the latest techniques in artificial intelligence and machine learning – is the next frontier to improve operations in clinical engineering functions.”
Changing the industry
Glassbeam plans to develop the new systems in phases. The first of these will focus on CT scanners, which are used to create cross-sectional views inside the human body without the need for surgery.
CT systems can cost up to $2.5 million apiece, and when they malfunction or need parts replacing, healthcare providers risk running up six-figure bills, while taking life-saving systems offline.
Glassbeam said that hospitals and clinics are missing out on the predictive capabilities offered by machine learning and big data analytics.
Its new system can warn professionals about problems a week before they might occur. This “can alert clinical engineering staff to become proactive in avoiding unplanned downtime, saving costs, and averting patient re-scheduling at the last minute”, said the company.
Counting the benefits
“The parts replacement industry for the global installed base of medical imaging equipment is slated to be a $3.6 billion market in 2020,” said Puneet Pandit, co-founder and CEO of Glassbeam.
“With AI and ML applications based on analysing millions of sensor readings captured in the Glassbeam cloud each day, even with a modest 10 percent savings we are ready to make a significant dent on the underlying inefficiencies.”
Lise Getoor, professor of computer science at the University of California in Santa Cruz, praised the work being done by Glassbeam, which is a technology partner at UCSC’s D3 (Data, Discovery, Decisions) Center.
“The number of signals coming from connected machines in the IoT market surpassed the ability of humans to keep track of them years ago,” she said.
“I’m excited to see Glassbeam taking a leadership role in leveraging artificial intelligence to change the rules of the game for the healthcare market.”
Internet of Business says
AI’s predictive capabilities, together with technologies such as big data analytics, digital twins, and enterprise asset management (EAM) systems, could be transformative across many sectors as the IoT spreads.
CERN’s Large Hadron Collider, the largest machine ever built, is perhaps the leading example of the technology’s potential. Every single component in the CERN campus is logged in an EAM system as a digital twin, and predictive analytics help engineers predict failures and plan downtime for essential maintenance.
Just as important, the system tells them exactly where the problem lies: an important factor in large, complex systems. With lives at stake as well as big science, these connected technologies have significant potential.
SANTA CLARA, Calif., April 11, 2018 /PRNewswire/ -- Glassbeam, Inc., the premier machine data analytics company, announced today that UCSF Health (University of California, San Francisco) has selected Glassbeam to drive their clinical engineering analytics program. These innovations are aimed at revolutionizing the quality, consistency and efficiency of medical equipment in support of patient care. Glassbeam will collaborate with UCSF to not only deliver value from its proven CLEAN™ blueprint (Clinical Engineering Analytics) to manage major parts of the imaging fleet, but will also expand the solution to various other modalities such as Ultrasound, Cath Lab, and Physiological monitoring equipment.
UCSF Medical Center in San Francisco was ranked number five in the nation by US News & World Report in 2017, with national rankings notched in 15 adult specialties and 9 children's specialties. It also achieved the highest rating possible in 8 procedures or conditions. As one of the pioneers exclusively focused on health, UCSF is driven by the idea that when the best research, the best education and the best patient care converge, great breakthroughs are achieved. This animating idea is driving UCSF Health to partner with Glassbeam in a rapidly changing and evolving health care environment in which UCSF is bringing innovative solutions to meet the growing needs of its patients and the communities it serves.
"Information Services and Analytics fits well within UCSF Health's 2020 strategic goals," said Ramana Sastry, Director of Clinical Engineering at UCSF Health. "Investing in data systems and predictive analytics capabilities to help us facilitate service management, asset utilization and performance improvement of medical machines is critical to our success. We are confident Glassbeam's unique analytics solution will help us tremendously as we scale our operations over next few years."
"The next steps to fulfill the promise of patient care has to include technical and customer service efficiencies in the form of predictive maintenance and machine learning intelligence," said Frank Beltré, Founder and Service Operations Consultant of QDC Biomedical, LLC. "Glassbeam's CLEAN provides machine learning to assist in managing equipment service within a sustainable cost-containment service delivery model. Injecting machine learning into medical equipment service operations will additionally enhance patient care by increasing equipment reliability and availability for medical diagnosis."
"The health care industry is eager to adopt new cutting-edge solutions that bring the rigor and openness of machine data analytics to the world of imaging and bio medical equipment," said Puneet Pandit, Co-founder and CEO at Glassbeam. "Glassbeam is at the forefront of this disruption. We are thrilled to partner with UCSF Health in providing innovative, high-quality, cost-competitive clinical services, and delivering for them an unparalleled patient experience across the entire care continuum."
Glassbeam is the premier machine data analytics company bringing structure and meaning to complex data generated from any connected machine in the Industrial IoT industry. Funded by several ultra-high net worth investors, Glassbeam's next generation cloud-based platform is designed to transform and analyze multi-structured data, delivering powerful solutions on customer support and product intelligence for companies such as IBM, Dell EMC, Novant Health, and Dimension Data. For more information visit http://www.glassbeam.com or follow us on Twitter @Glassbeam.
Thu12Apr2018By Fred Bazzoli
Healthcare organizations spend millions of dollars on imaging devices, so ensuring that they’re optimally maintained is essential in maximizing the return on that investment. Now, predictive analytics and machine learning are being used to do that.
UCSF Medical Center in San Francisco is turning to an information services and analytics product from Glassbeam to power its clinical engineering analytics program.
The hospital will work with the Santa Clara, Calif.-based company to use its CLEAN blueprint (Clinical Engineering Analytics) to manage components of its imaging equipment, with plans to expand its use to other modalities, such as ultrasound, cath lab and physiological monitoring equipment.
“Investing in data systems and predictive analytics capabilities to help us facilitate service management, asset utilization and performance improvement of medical machines is critical to our success,” says Ramana Sastry, director of clinical engineering at UCSF Health. “Glassbeam’s unique analytics solution will help us as we scale our operations over next few years.”
UCSF executives say that imaging medical equipment systems are based on complex technologies, and they increasingly are producing complex machine data that require more advanced data transformation solutions to enable root cause analysis, predictive analytics, machine learning and other high-value support applications.
The Glassbeam technology is intended to help organizations realize value from their machine data, and it can be used to optimize uptime on a variety of devices from different manufacturers, analyzing data to give a better view of operations and provide actionable intelligence.
Using machine learning to improve imaging device performance is a crucial next step, says Frank Beltre, a service operations management consultant for UCSF. “This process traditionally has been done manually, and we’ve had to inject human behavior into the process of gathering data and looking at data. Using predictive analytics for lifecycle management of equipment is much easier—using manual processes doesn’t provide the predictive piece. It can take two to three weeks to analyze data from these devices, and so automating the gathering and analysis of this data can help you predict what to do and be ready for future events.”
Glassbeam’s analytics and data collection runs on Amazon’s cloud services, says Puneet Pandit, the company’s CEO. Service logs from imaging devices are extensive but often result in vast quantities of unstructured data that contains a lot of semantic meaning. Digesting the output of these devices can help improve service and provide better care to patients, he says.
Time savings in managing complex imaging devices can be significant, Beltre says. Predicting part failure or wise use of preventive maintenance can result in huge time savings. If a part fails in an imaging device, it can take 40 hours to obtain the replacement, install it and test it, he says. Getting ahead of part failure can increase device uptime and reduce costs for procuring replacement parts, he says.
Founded in 2010 as an online loyalty card service, Punchh has since grown into a marketing platform serving more than 115 restaurant chains, including Pizza Hut and Quiznos. Now it’s raised a $20 million Series B to expand into more retail verticals and increase the use of artificial intelligence and machine learning in its cloud software. The funding was led by Sapphire Ventures, with participation from returning investor Cervin Ventures.
Along with its angel and Series A financing, this brings Punchh’s total funding so far to about $31 million. The startup says its goal is to give brick-and-mortar stores the same level of data analytics as e-commerce giants like Amazon.
Punchh’s platform enables restaurants to digitize their customer loyalty programs and complements that with tools like Punchh Acquire, which is designed to help businesses turn casual customers into regulars by promoting offers through multiple channels, including email, SMS, social media, Apple Pay and eClub.
The company currently has 145 employees and is based in San Mateo, California, with offices in Austin, Texas and Delhi. This is Punchh’s first funding announcement in three years and the startup’s largest round of financing by far (it raised $9.5 million Series A in 2015).
Co-founder and chief executive officer Shyam Rao says the time was right for Punchh to raise again because it already serves many of the biggest restaurant chains, with 34,000 locations between them, and wanted to tap into demand from retailers in other verticals.
Punchh is now focusing on convenience stores, gas stations and health and beauty brands (clients already include Fantastic Sams hair salons and TruFusion, a chain of fitness studios). The company competes with other digital loyalty and marketing platforms like Stamp Me, LoyalZoo and Stocard. Rao says Punchh’s ability to create campaigns that target a very specific audience sets it apart from rivals. Punchh’s algorithms pulls together data from several sources, including event calendars, weather, local demographics and the purchasing history of individual customers, for what it describes as “micro-moment marketing.”
For example, if cold weather is expected over a holiday weekend, it might send offers for a discounted hot soup and tea set to mothers between the ages of 30 to 55. Punchh claims it increases spending at its customers’ restaurants by 10% to 20%.
“Imagine trying to manage that process of using mountains of data to build customer relationships and tailor every experience, at scale across hundreds of locations. That’s what Punchh does,” says Rao.
In a statement, Jai Das, Sapphire Ventures managing director said “Punchh is already a global leader in digital marketing solutions for restaurants, which alone would be a fantastic reason to invest in the company, but the scope of their technology goes far beyond just restaurants and encompasses all brick-and-mortar stores with a POS.”